Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Non-redundant sequential rules-Theory and algorithm
Information Systems
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Prediction mining – an approach to mining association rules for prediction
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
The TIMERS II algorithm for the discovery of causality
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A cognitive tutoring agent with episodic and causal learning capabilities
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
A computational model for causal learning in cognitive agents
Knowledge-Based Systems
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Mining sequential rules common to several sequences with the window size constraint
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Mining association rules for the quality improvement of the production process
Expert Systems with Applications: An International Journal
Towards assisted input and output data analysis in manufacturing simulation: the EDASim approach
Proceedings of the Winter Simulation Conference
Closeness Preference - A new interestingness measure for sequential rules mining
Knowledge-Based Systems
TNS: mining top-k non-redundant sequential rules
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Exploring Sequential and Association Rule Mining for Pattern-based Energy Demand Characterization
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
CSSF-trie structure to mine constraint sequential patterns from progressive database
International Journal of Knowledge Engineering and Data Mining
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Mining sequential rules from large databases is an important topic in data mining fields with wide applications. Most of the relevant studies focused on finding sequential rules appearing in a single sequence of events and the mining task dealing with multiple sequences were far less explored. In this paper, we present RuleGrowth, a novel algorithm for mining sequential rules common to several sequences. Unlike other algorithms, RuleGrowth uses a pattern-growth approach for discovering sequential rules such that it can be much more efficient and scalable. We present a comparison of RuleGrowth's performance with current algorithms for three public datasets. The experimental results show that RuleGrowth clearly outperforms current algorithms for all three datasets under low support and confidence threshold and has a much better scalability.